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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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YOLOv8-G2F:一个便携式的手势识别优化算法.

Zhao Feng1, Junjian Huang1, Wei Zhang1

  • 1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

Neural networks : the official journal of the International Neural Network Society
|April 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了YOLOv8-G2F,这是一种轻量级的深度学习模型,用于快速准确的手势识别 (HGR). 它在nus-ii数据集上达到99.2%的准确性,模型大小小小2.33 MB,增强了HGR应用程序.

关键词:
深度学习是一种深度学习.在深度上可分离的卷积.集团卷积 集团卷积是指集团的卷积.在HGR中使用HGR.轻量级的网络设计.模型修剪 模型修剪在 YOLOv8 网络中,YOLOv8 网络是

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 手势识别 (HGR) 对于人机交互至关重要,但由于硬件成本高,模型尺寸大,它面临着挑战.
  • 深度学习已经推进了HGR,但对于端到端的应用,需要轻量级,高精度的网络.

研究的目的:

  • 开发一个高效准确的轻量级网络,用于实时手势识别.
  • 为了减少HGR模型的计算和内存足迹,而不会牺牲性能.

主要方法:

  • 推出了YOLOv8-G2F,这是基于YOLOv8的增强网络,包含轻量级模块,取代了传统的卷积.
  • 采用线性转换,组卷积和深度可分离卷积来实现高效的特征提取.
  • 使用模型修剪来进一步减少模型大小并提高准确性.

主要成果:

  • 在nus-ii手势数据集上实现了99.2%的识别准确度.
  • 由此产生的模型大小仅为2.33 MB,证明了显著的效率.
  • 在比较和废除研究中,YOLOv8-G2F显示了与现有算法相比的显著改进.

结论:

  • YOLOv8-G2F为手势识别提供了一个高度准确和轻量级的解决方案.
  • 拟议的网络有效地满足了对高效端到端HGR系统的需求.
  • 这项工作为HGR在资源有限的环境中的更广泛应用铺平了道路.